Additive Genetic Variance Research Articles

Genetic and genomic evaluation of age at first calving in Italian Holsteins

Age at first calving (AFC) represents the non-productive period of around 2 yr in Holstein cows and thus it has a relevant impact on the cost of rearing replacements in the dairy herd. In the present study we aimed at evaluating genetic and genomic aspects of AFC in the Italian Holstein population. Data of 4,206,218 heifers with first calving between 1996 and 2020 were used. Age at first calving averaged 26.09 ± 3.07 mo and decreased across years. Heritability was estimated using a linear animal model which included the fixed effects of herd-year-season of birth and classes of gestation length, and the random animal additive genetic effect fitted to a pedigree-based relationship matrix. Estimated breeding values (EBV) and genomically enhanced breeding values (GEBV) were obtained, and they were standardized to mean 100 and standard deviation 5, where animals above the mean are those contributing to reduce AFC. Heritability estimates of AFC ranged from 0.031 to 0.045. The trend of sires' GEBV was favorable and indicated a reduced AFC across years. Approximate genetic correlations between GEBV of AFC and GEBV of other economically important traits were calculated on a subset of genotyped females born after 2015. Moderate favorable associations of AFC with production traits (0.39 to 0.51), udder depth (0.40), interval from first to last insemination in heifer (-0.43), and longevity (0.34) were assessed. Overall, the greatest lifetime productive performances and most favorable days open in first lactation were observed when heifers calved at 22-23 mo. On the other hand, progeny of sires with GEBV of AFC above the mean yielded more milk, fat, and protein in first lactation, and had shorter days open than progeny of sires with GEBV of AFC below the mean. Results suggested that breeding strategies to improve AFC should be pursued, also considering genetic correlations between AFC and traits which are already part of the Italian Holstein breeding objective. The inclusion of AFC in an aggregate index is expected to contribute to enhance farm income.

Vertically inherited microbiota and environment modifying behaviours conceal genetic variation in dung beetle life history.

Diverse organisms actively manipulate their (sym)biotic and physical environment in ways that feed back on their own development. However, the degree to which these processes affect microevolution remains poorly understood. The gazelle dung beetle both physically modifies its ontogenetic environment and structures its biotic interactions through vertical symbiont transmission. By experimentally eliminating (i) physical environmental modifications and (ii) the vertical inheritance of microbes, we assess how environment modifying behaviour and microbiome transmission shape heritable variation and evolutionary potential. We found that depriving larvae of symbionts and environment modifying behaviours increased additive genetic variance and heritability for development time but not body size. This suggests that larvae's ability to manipulate their environment has the potential to modify heritable variation and to facilitate the accumulation of cryptic genetic variation. This cryptic variation may become released and selectable when organisms encounter environments that are less amenable to organismal manipulation or restructuring. Our findings also suggest that intact microbiomes, which are commonly thought to increase genetic variation of their hosts, may instead reduce and conceal heritable variation. More broadly, our findings highlight that the ability of organisms to actively manipulate their environment may affect the potential of populations to evolve when encountering novel, stressful conditions.

Analysis of Combining Ability to Obtain Tropical Carrot Hybrids for Production Traits

Carrots (Daucus carota L.), a globally significant vegetable, lack extensive research on heterotic groups and diallel analysis to generate hybrid combinations. Thus, the objective of this study was to assess combining abilities and identify optimal carrot parents for producing hybrids suitable for tropical climates with elevated metabolite levels. Twenty carrot hybrids, ten parent plants, and three commercial cultivars were evaluated during the summers of 2020/2021 and 2021/2022. Agronomic evaluations were carried out and chlorophyll and carotenoid levels were determined, followed by a diallel analysis using Griffing’s Method III and GGE biplot analysis. There were significant general combining ability (GCA) effects for various agronomic traits, suggesting additive genetic effects. Based on GCA, cultivars 5, 4, and 2 were the most promising parents. Specific combining ability (SCA) revealed that hybrids 1 × 2 and 3 × 5 stood out in environment 1, whereas hybrids 1 × 5 and 5 × 3 performed well in environment 2. The GGE biplot analysis showed that hybrids 1 × 2 and 3 × 2 displayed larger average root diameters, belonged to the group with the best bolting percentages, and exhibited stability across environments. Moreover, hybrids 2 × 4, 3 × 1, 4 × 1, and 4 × 2 exhibited higher metabolite levels. These findings suggest the feasibility of obtaining superior hybrids tailored for the tropical carrot market.

The Estimation of Genetic Parameters for Chronic Progressive Lymphedema and Body Traits in the Rhenish German Draught Horse.

Chronic progressive lymphedema (CPL) is a prevalent and progressive disease in Rhenish German draught horses. The objective of our follow-up study was to evaluate the heritability of this disease in Rhenish German draught horses using pedigree-based and genomic relationship matrices. We employed linear and threshold animal models. Models included the random animal effect and effects of breeding association, coat colour, sex, and age within sex, and farm-related factors, on CPL scores. In addition, we estimated heritabilities in models assuming censoring for data when horses were below an age of 1-15 years. The heritabilities of CPL scores across all ages were 0.595 ± 0.131 and 0.482 ± 0.105 in the threshold and linear animal model with pedigree-based relationship matrices, respectively. The restriction of data to horses with a minimum age at examination or accounting for censored data in younger animals showed an increase in heritabilities of CPL scores up to 0.788 ± 0.168 (threshold model) and 0.752 ± 0.153 (linear model) at an age of 7-8 years. Analyses including genomic relationship matrices yielded very similar estimates, but with smaller standard errors than pedigree-based analyses. Heritabilities in threshold models for CPL prevalence (CPL-bin-score) and the number of affected limbs (CPL-bin-sum) were 0.176-0.189 ± 0.061-0.064 and 0.375-0.433 ± 0.164-0.170, respectively. We were able to show moderately to highly positive genetic correlations between the CPL score and cannon bone circumference (0.529-0.825), height at withers (0.338-0.555), and skinfold thickness (0.241-0.517). Using the dichotomous trait for the CPL score and the genomic relationship matrix resulted in corresponding estimates of 0.868, 0.793, and 0.784, respectively. This study showed the great importance of additive genetic variation influencing the expression of chronic progressive lymphedema in Rhenish German draught horses. Therefore, further research is warranted to implement breeding programmes in a small breeding population that exploit the potential of additive genetic differences among animals for reducing the prevalence and severity of lesions of this incurable disease.

Genetic parameters for mesenteric torsion in a pig maternal line

Mesenteric torsion (MT) is a condition characterized by the twisting of intestinal loops, leading to a disruption in blood flow and abdominal distension. Several factors can predispose animals to this condition, which primarily affects healthy, fast-growing pigs and often results in fatal outcomes. Given the absence of studies on the genetic factors associated with this condition, our research aimed to estimate the heritability coefficient for MT and explore the genetic correlations between MT and performance traits in pigs. Our dataset comprised of 43,384 records of pigs from a maternal line from a nucleus farm. These pigs were born between 2010 and 2022 and had recorded information regarding MT status (0 for healthy and 1 for affected animals), weaning weight (WW), as well as pedigree, genotypes, and genomic estimated breeding values (GEBVs) for the following traits: MT status, days to reach 110 kg (D110), feed conversion adjusted to 110 kg (FC110), and backfat thickness adjusted to 110 kg (BT110). In our statistical model, the effects of contemporary group (CG: sex, year, and week of weaning) and the linear covariate WW were considered fixed effects, while the direct additive genetic effect was accounted as random. On average, pigs affected by MT had an age of 104.35 ± 37.75 days, and the condition had an overall herd frequency of 2.46 %. Mesenteric torsion occurred more frequently in female pigs raised in collective pens compared to non-castrated males raised in individual pens. Heritability estimates for MT, using both pedigree (0.13 ± 0.02) and genomic (0.12 ± 0.02) data, were of low magnitude. This suggests that while there is genetic variability associated with MT, a significant portion of this variation is due to environmental variance. The correlation between MT and BT110 GEBVs was of low magnitude (0.05). In contrast, the correlations between GEBVs for MT and both D110 and FC110 were moderate in magnitude (0.22 for both traits). This implies that pigs taking longer to reach 110 kg and those with poorer feed conversion are more likely to develop MT. There is genetic variability for mesenteric torsion in pigs. Selection aimed at improving feed conversion and reducing the age at which pigs reach 110 kg could lead to a decrease in the occurrence of MT within pig populations.

Breeding Strategies for Simultaneous Improvement in Anthracnose Disease Resistance and Economically Important Traits in French Bean

Background: French bean (Phaseolus vulgaris L.) production in the tropics is threatened by heavy incidence of anthracnose disease causing substantial crop loss when infection is at the early growth stages. Breeding strategies for tolerance against anthracnose disease along with enhanced productivity, and high pod protein content are to be formulated. Methods: Genotypes having broad genetic base and phenotypic diversity in 5 bush and 1 pole types were crossed in a 6 × 6 half diallel mating design to estimate combining ability, mode of gene action, and extent of heterosis for 11 quantitative traits. Result: The additive genetic effect was evident for percent disease index (PDI) of anthracnose and 10 pod weight. Rapid genetic gain can be achieved due to predominance of additive gene action in their control and can therefore be selected in early generation through simple breeding methods. Rest of the economic traits controlled by additive and non-additive gene effects could be improved through biparental mating, reciprocal recurrent selection, or diallel selective mating. Anthracnose disease tolerant and high yielding cultivars of might be developed utilizing parents, ‘Laxmi’, ‘Arka Sharath’ and ‘Vaishnavi-264’ with high gca effects. Although two cross combinations ‘Arka Sharath × Lakshmi’ and ‘Arjun × Arka Sharath’ showed significant heterobeltiosis in desired direction for PDI of anthracnose and other desirable horticultural traits but could not be exploited at commercial level due to complexity in hybridization. Identifying pure lines with tolerance against anthracnose disease and favorable horticultural attributes could be accomplished in segregating generations of the prospective hybrids.

Accounting for dominance genetic variation in carcass quality traits of Canadian crossbred beef cattle

The objectives of this study were to evaluate the contribution of additive and dominance genetic effects to the phenotypic variation of carcass quality traits and to identify the underlying genetic variants associated with these traits. A total of 3958 Canadian crossbred beef cattle with phenotype and genotype data were used in two models: (1) additive and (2) joint additive and dominance genomic models that included fixed contemporary group, and covariates of slaughter age, and the eigenvectors of five principal components to account for population structure. Variance components and genome-wide association analyses were performed, and a 10% genome-wide false discovery rate (FDR) was applied to declare associations as significant. Genomic heritability ranged from 0.31 ± 0.03 for ultrasound rib eye area to 0.46 ± 0.05 for marbling score. Up to 10% dominance genetic variation was observed for ultrasound rib eye area and marbling score, indicating the contribution of dominance genetic effects to these trait variations. Eleven overlapping significant single-nucleotide polymorphism associations were identified across the studied traits and models. The identified candidate genes (e.g., BTC, SPP1, and SEPSECS) have biological functions related to tissue growth and skeletal muscle development and can be further validated in other cattle populations to determine their usefulness for beef cattle genetic improvement.

Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models

The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7–3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0–9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0–3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

Milk urea nitrogen is genetically associated with production and reproduction performance of dairy cows: A meta-analysis

This study aimed to conduct a meta-analysis using the random-effects model to integrate published estimates of genetic parameters for milk urea (MU) and milk urea nitrogen (MUN). The study used a total of 49 heritability and 147 genetic correlation estimates from 30 papers published between 2003 and 2023. The heritability estimates for MU and MUN were low and equal to 0.202 and 0.181, respectively. The genetic correlation estimates between MU and production traits were low and ranged from -0.039 (between MU and milk lactose percentage) to 0.102 (between MU and milk protein yield), but the genetic correlation estimates between MUN and production traits were low and varied from -0.070 (between MUN and somatic cell score) to 0.357 (between MUN and milk protein yield). The genetic correlation estimates between MUN and calving to first service, days open, and first service conception rate were low and equal to 0.093, 0.329, and 0.069, respectively. The results of present study showed that available additive genetic variation in MU and MUN could potentially be exploited in dairy cow genetic selection schemes. Although the correlations of MU and MUN with production and reproduction traits were low, there were some cases where they were unfavorable, and they should not be neglected when planning selection programs.

Genetic gains underpinning a little-known strawberry Green Revolution

The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.

Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model

Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis niloticus)

This study aimed to verify the possibility of obtaining genetic gain by selecting breeders based on genetic parameter estimates for reproductive performance traits of female tilapia (Oreochromis sp.) from a commercial population. The study was conducted using a database containing information on reproductive traits collected from 2009 to 2021. During the breeding period, the animals were stocked in tanks in a 1:3 male: female ratio. Eggs were collected from the mouth of the females every five days, and the collected eggs were measured in 100 mL beakers to estimate the egg volume. To estimate the variance components and genetic parameters (heritability, correlation, and repeatability), a mixed animal model was used with the fixed effects of the production cycle and breeding tanks, in addition to the additive direct genetic and permanent environmental random effects. The heritability values were 0.26 ± 0.14, 0.18 ± 0.10, 0.15 ± 0.09, and 0.13 ± 0.08 for total egg volume, average egg volume, spawning frequency, and average spawning interval, respectively, with repeatability above 0.43 for all analyzed variables, demonstrating the ability of the evaluated breeds to maintain the same production level during the different cycles. The genetic correlations among the variables were high, with a correlation of −0.99 between the total egg volume and the average spawning interval. Thus, selecting for total egg volume, which had the highest heritability, would result in animals that produce a large volume of eggs and have short spawning intervals.

Components of Genetic Variation for Yield and its Attributing Traits in Bottle Gourd (Lagenaria siceraria (Mol.) Standl.)

An investigation was carried out to assess the gene action in bottle gourd for twenty- three characters at Main Experiment Station, Department of Vegetable Science, A.N.D.U.A.&amp;T., Kumarganj, Ayodhya (U.P). during Zaid 2022 using 12 genotypes, nine lines i.e., Narendra Rashmi, NDBG-17, Pusa Naveen, NDBG-21, Punjab Komal, NDBG-28, NDBG-65-2, NDBG-105, NDBG-Sel-2 and three testers i.e., Narendra Prabha, Narendra Kamna, Narendra Pooja and their 27 hybrids obtained through line × tester mating pattern. The variances due to sca (σ2 s) were higher than variance due to gca (σ2g) for all the characters. The value of dominance genetic variances (σ2 D) were larger than additive genetic variance (σ2 A), indicating the presence of dominance and over dominance effects and preponderance of non-additive gene action .The heritability in narrow sense was low for most of the traits except fruit length, days to first staminate flower anthesis and primary branches per plant where high estimate of heritability in narrow sense with low genetic advance were recorded, indicating the presence of non-additive gene action accompanied by environmental influence.

Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.

Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes

The objective of the present study was to identify genomic regions influencing economic traits in Murrah buffaloes using weighted single step Genome Wide Association Analysis (WssGWAS). Data on 2000 animals, out of which 120 were genotyped using a double digest Restriction site Associated DNA (ddRAD) sequencing approach. The phenotypic data were collected from NDRI, India, on growth traits, viz., body weight at 6M (month), 12M, 18M and 24M, production traits like 305D (day) milk yield, lactation length (LL) and dry period (DP) and reproduction traits like age at first calving (AFC), calving interval (CI) and first service period (FSP). The biallelic genotypic data consisted of 49353 markers post-quality check. The heritability estimates were moderate to high, low to moderate, low for growth, production, reproduction traits, respectively. Important genomic regions explaining more than 0.5% of the total additive genetic variance explained by 30 adjacent SNPs were selected for further analysis of candidate genes. In this study, 105 genomic regions were associated with growth, 35 genomic regions with production and 42 window regions with reproduction traits. Different candidate genes were identified in these genomic regions, of which important are OSBPL8, NAP1L1 for growth, CNTNAP2 for production and ILDR2, TADA1 and POGK for reproduction traits.

Re-sequencing of the casein genes in Swedish Red cattle giving milk with diverse protein profiles and extreme rennet coagulation properties

Impaired rennet coagulation properties in milk could lead to prolonged processing times and production losses. Heritability for milk coagulation has previously been estimated to be 0.28 to 0.45, indicating that genetic selection can be used to manipulate this trait. The CN proteins are expressed by the genes CSN1S1, CSN2, CSN1S2, and CSN3 and are located on bovine chromosome 6. To better understand the effect of genetic variation in the CN genes on milk coagulation, blood and milk samples from 30 Swedish Red Dairy Cattle (RDC) with divergent coagulation properties were investigated. DNA from the 30 cows was sequenced for the CN genes to determine the theoretical AA sequence and to look for genetic variation in the untranslated regions. The aim is to confirm the protein genetic variants previously reported, while searching for additional genetic variation in the CN genes of 30 RDC. We observed genetic variation in 116 SNPs in the known CN genes where 10% of the SNPs are exon variants and the remaining 90% are intron variants. A total of 2.5% of the SNPs are found in the 5′- or 3′-untranslated region (UTR) regions of the exons; 2% are synonymous variants and 6% are missense variants that concurred with the known protein variants for CSN1S1, CSN2, and CSN3. Furthermore, 6% of the SNPs are splice polypyrimidine tract intron variants. The 2 genetic variants in the 5′- and 3′-UTR in CSN1S1 and CSN3 are found with protein variants CSN1S1C and CSN3B. Because both UTR variants are associated with gain and loss of micro RNA and transcription factors, this could explain differences in expression of the genetic protein variants. Preliminary chi-squared analysis and comparison with previous GWAS studies showed potential connections between the identified SNPs and coagulation properties of milk. By advancing the knowledge of the connection between the DNA sequence and the functional properties of the CN proteins, we hope to learn more about the cheese coagulation properties of milk from RDC.

Predicting bovine daily milk yield by leveraging genomic breeding values

The main goal of this work, conducted on a herd of 502 Holstein cows situated in Italy, is to propose a machine learning-based approach to forecast the individual bovine daily milk production by explicitly leveraging genotypic information. As part of our study, we also evaluated the importance in the prediction of genotypic and phenotypic variables usually available within herd. The methodology we propose is based on two consecutive models: a genomic prediction one to calculate the animal’s genomic breeding value from marker data, followed by a feed forward neural network combining such additive genetic effect and the environmental features (parity, days in milk, age at calving in months, month of calving) for milk yield forecasting. In particular, we both assess the inclusion of genomic breeding values calculated within herd or provided by the breeders’ association, discovering that the latter ones allow for better final predictions. The results of our model outperform the ones by a linear mixed model with the same inputs on average, day by day and at the individual level. Moreover, we propose a problem formulation that also leverages additional factors partially controllable by breeders: in this case, features such as the number of milkings and the concentrate consumption inside the automatic milking system prove to highly impact on the final prediction, and hence on milk production. To the best of our knowledge, the proposed problem formulation based on genomic breeding values is a novelty in the individual bovine milk yield machine learning forecasting literature. Given the low genotyping costs and the availability of a larger number of environmental features in farms equipped with a wide range of sensors, as automatic milking systems, our solution can support breeders’ herd management and animal monitoring, thanks to the possibility to forecast the full lactation curve in advance even for primiparous bovines and newborn calves. With this work, we successfully achieve our objectives of including genomic information in bovine milk yield machine learning-based forecasting, thus improving the performance on this task, and of evaluating the impact on prediction of common genotypic and phenotypic information available to breeders.

Adult body mass is heritable, positively genetically correlated and under selection of differing shapes between the sexes in a bird with little apparent sexual dimorphism.

In most animals, body mass varies with ecological conditions and is expected to reflect how much energy can be allocated to reproduction and survival. Because the sexes often differ in their resource acquisition and allocation strategies, variations in adult body mass and their consequences on fitness can differ between the sexes. Assessing the relative contributions of environmental and genetic effects (i.e. heritability)-and whether these effects and their fitness consequences are sex-specific-is essential to gain insights into the evolution of sexual dimorphism and sexual conflicts. We used 20+ years of data to study the sources of variation in adult body mass and associated fitness consequences in a bird with biparental care, the Alpine swift (Tachymarptis melba). Swifts appear monomorphic to human observers, though subtle dimorphisms are present. We first investigated the effects of weather conditions on adult body mass using a sliding window analysis approach. We report a positive effect of temperature and a negative effect of rainfall on adult body mass, as expected for an aerial insectivorous bird. We then quantified the additive genetic variance and heritability of body mass in both sexes and assessed the importance of genetic constraints on mass evolution by estimating the cross-sex genetic correlation. Heritability was different from zero in both sexes at ~0.30. The positive cross-sex genetic correlation and comparable additive genetic variance between the sexes suggest the possibility for evolutionary constraints when it comes to body mass. Finally, we assessed the sex-specific selection on adjusted body mass using multiple fitness components. We report directional positive and negative selection trending towards stabilizing and diversifying selection on females and males respectively in relation to the weighted proportion of surviving fledglings. Overall, these results suggest that while body mass may be able to respond to environmental conditions and evolve, genetic constraints would result in similar changes in both sexes or an overall absence of response to selection. It remains unclear whether the weak (1%) dimorphism in Alpine swift body mass we report is simply a result of the similar fitness peaks between the sexes or of genetic constraints.

Directional epistasis is common in morphological divergence.

Epistasis is often portrayed as unimportant in evolution. While random patterns of epistasis may have limited effects on the response to selection, systematic directional epistasis can have substantial effects on evolutionary dynamics. Directional epistasis occurs when allele substitutions that change a trait also modify the effects of allele substitution at other loci in a systematic direction. In this case, trait evolution may induce correlated changes in allelic effects and additive genetic variance (evolvability) that modify further evolution. Although theory thus suggests a potentially important role for directional epistasis in evolution, we still lack empirical evidence about its prevalence and magnitude. Using a new framework to estimate systematic patterns of epistasis from line-crosses experiments, we quantify its effects on 197 size-related traits from diverging natural populations in 24 animal and 17 plant species. We show that directional epistasis is common and tends to become stronger with increasing morphological divergence. In animals, most traits displayed negative directionality toward larger size, suggesting that epistatic constraints reducing evolvability toward larger size. Dominance was also common but did not systematically alter the effects of epistasis.